mindspore.dataset.vision.c_transforms.SoftDvppDecodeRandomCropResizeJpeg

class mindspore.dataset.vision.c_transforms.SoftDvppDecodeRandomCropResizeJpeg(size, scale=(0.08, 1.0), ratio=(0.75, 1.3333333333333333), max_attempts=10)[source]

Tensor operation to decode, random crop and resize JPEG image using the simulation algorithm of Ascend series chip DVPP module.

The usage scenario is consistent with SoftDvppDecodeResizeJpeg. The input image size should be in range [32*32, 8192*8192]. The zoom-out and zoom-in multiples of the image length and width should in the range [1/32, 16]. Only images with an even resolution can be output. The output of odd resolution is not supported.

Parameters

size (Union[int, sequence]) – The size of the output image. If size is an integer, a square crop of size (size, size) is returned. If size is a sequence of length 2, it should be (height, width).
scale (tuple, optional) – Range [min, max) of respective size of the original size to be cropped (default=(0.08, 1.0)).
ratio (tuple, optional) – Range [min, max) of aspect ratio to be cropped (default=(3. / 4., 4. / 3.)).
max_attempts (int, optional) – The maximum number of attempts to propose a valid crop_area (default=10). If exceeded, fall back to use center_crop instead.

Examples

>>> import mindspore.dataset.vision.c_transforms as c_vision
>>>
>>> # decode, randomly crop and resize image, keeping aspect ratio
>>> transforms_list1 = [c_vision.Decode(), c_vision.SoftDvppDecodeRandomCropResizeJpeg(90)]
>>> data1 = data1.map(operations=transforms_list1, input_columns=["image"])
>>> # decode, randomly crop and resize to landscape style
>>> transforms_list2 = [c_vision.Decode(), c_vision.SoftDvppDecodeRandomCropResizeJpeg((80, 100))]
>>> data2 = data2.map(operations=transforms_list2, input_columns=["image"])